Overview

Dataset statistics

Number of variables24
Number of observations9426
Missing cells72
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory192.0 B

Variable types

Numeric11
Categorical8
Text3
DateTime2

Alerts

Row ID has unique valuesUnique
Discount has 848 (9.0%) zerosZeros

Reproduction

Analysis started2024-07-05 12:03:56.252392
Analysis finished2024-07-05 12:04:09.490743
Duration13.24 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Row ID
Real number (ℝ)

UNIQUE 

Distinct9426
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20241.015
Minimum2
Maximum26399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:09.624570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4040.5
Q119330.25
median21686.5
Q324042.75
95-th percentile25927.75
Maximum26399
Range26397
Interquartile range (IQR)4712.5

Descriptive statistics

Standard deviation6101.891
Coefficient of variation (CV)0.3014617
Kurtosis2.9998152
Mean20241.015
Median Absolute Deviation (MAD)2356.5
Skewness-1.9363104
Sum1.9079181 × 108
Variance37233073
MonotonicityNot monotonic
2024-07-05T17:34:09.726823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19914 1
 
< 0.1%
4561 1
 
< 0.1%
25101 1
 
< 0.1%
21561 1
 
< 0.1%
21058 1
 
< 0.1%
25099 1
 
< 0.1%
23922 1
 
< 0.1%
23923 1
 
< 0.1%
25622 1
 
< 0.1%
23931 1
 
< 0.1%
Other values (9416) 9416
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
27 1
< 0.1%
52 1
< 0.1%
53 1
< 0.1%
62 1
< 0.1%
63 1
< 0.1%
64 1
< 0.1%
66 1
< 0.1%
67 1
< 0.1%
68 1
< 0.1%
ValueCountFrequency (%)
26399 1
< 0.1%
26398 1
< 0.1%
26397 1
< 0.1%
26396 1
< 0.1%
26395 1
< 0.1%
26394 1
< 0.1%
26393 1
< 0.1%
26392 1
< 0.1%
26391 1
< 0.1%
26390 1
< 0.1%

Order Priority
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
High
1970 
Low
1926 
Not Specified
1881 
Medium
1844 
Critical
1804 

Length

Max length13
Median length8
Mean length6.7489921
Min length3

Characters and Unicode

Total characters63616
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNot Specified
2nd rowCritical
3rd rowCritical
4th rowCritical
5th rowLow

Common Values

ValueCountFrequency (%)
High 1970
20.9%
Low 1926
20.4%
Not Specified 1881
20.0%
Medium 1844
19.6%
Critical 1804
19.1%
Critical 1
 
< 0.1%

Length

2024-07-05T17:34:09.815113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T17:34:09.919557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
high 1970
17.4%
low 1926
17.0%
not 1881
16.6%
specified 1881
16.6%
medium 1844
16.3%
critical 1805
16.0%

Most occurring characters

ValueCountFrequency (%)
i 11186
17.6%
e 5606
 
8.8%
o 3807
 
6.0%
d 3725
 
5.9%
c 3686
 
5.8%
t 3686
 
5.8%
H 1970
 
3.1%
g 1970
 
3.1%
h 1970
 
3.1%
L 1926
 
3.0%
Other values (13) 24084
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50427
79.3%
Uppercase Letter 11307
 
17.8%
Space Separator 1882
 
3.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 11186
22.2%
e 5606
11.1%
o 3807
 
7.5%
d 3725
 
7.4%
c 3686
 
7.3%
t 3686
 
7.3%
g 1970
 
3.9%
h 1970
 
3.9%
w 1926
 
3.8%
f 1881
 
3.7%
Other values (6) 10984
21.8%
Uppercase Letter
ValueCountFrequency (%)
H 1970
17.4%
L 1926
17.0%
S 1881
16.6%
N 1881
16.6%
M 1844
16.3%
C 1805
16.0%
Space Separator
ValueCountFrequency (%)
1882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61734
97.0%
Common 1882
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 11186
18.1%
e 5606
 
9.1%
o 3807
 
6.2%
d 3725
 
6.0%
c 3686
 
6.0%
t 3686
 
6.0%
H 1970
 
3.2%
g 1970
 
3.2%
h 1970
 
3.2%
L 1926
 
3.1%
Other values (12) 22202
36.0%
Common
ValueCountFrequency (%)
1882
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 11186
17.6%
e 5606
 
8.8%
o 3807
 
6.0%
d 3725
 
5.9%
c 3686
 
5.8%
t 3686
 
5.8%
H 1970
 
3.1%
g 1970
 
3.1%
h 1970
 
3.1%
L 1926
 
3.0%
Other values (13) 24084
37.9%

Discount
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.049627626
Minimum0
Maximum0.25
Zeros848
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:10.023528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median0.05
Q30.08
95-th percentile0.1
Maximum0.25
Range0.25
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.031798425
Coefficient of variation (CV)0.64074041
Kurtosis-0.9872078
Mean0.049627626
Median Absolute Deviation (MAD)0.03
Skewness0.072044551
Sum467.79
Variance0.0010111398
MonotonicityNot monotonic
2024-07-05T17:34:10.105415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.01 898
9.5%
0.03 882
9.4%
0.05 879
9.3%
0.09 871
9.2%
0.02 870
9.2%
0.04 861
9.1%
0.08 850
9.0%
0 848
9.0%
0.1 838
8.9%
0.06 821
8.7%
Other values (6) 808
8.6%
ValueCountFrequency (%)
0 848
9.0%
0.01 898
9.5%
0.02 870
9.2%
0.03 882
9.4%
0.04 861
9.1%
0.05 879
9.3%
0.06 821
8.7%
0.07 803
8.5%
0.08 850
9.0%
0.09 871
9.2%
ValueCountFrequency (%)
0.25 1
 
< 0.1%
0.21 1
 
< 0.1%
0.17 1
 
< 0.1%
0.16 1
 
< 0.1%
0.11 1
 
< 0.1%
0.1 838
8.9%
0.09 871
9.2%
0.08 850
9.0%
0.07 803
8.5%
0.06 821
8.7%

Unit Price
Real number (ℝ)

Distinct751
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.303686
Minimum0.99
Maximum6783.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:10.197761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.99
5-th percentile2.88
Q16.48
median20.99
Q385.99
95-th percentile320.64
Maximum6783.02
Range6782.03
Interquartile range (IQR)79.51

Descriptive statistics

Standard deviation281.54098
Coefficient of variation (CV)3.1883265
Kurtosis276.52846
Mean88.303686
Median Absolute Deviation (MAD)17.01
Skewness14.135146
Sum832350.54
Variance79265.324
MonotonicityNot monotonic
2024-07-05T17:34:10.292506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.48 298
 
3.2%
65.99 219
 
2.3%
4.98 153
 
1.6%
125.99 131
 
1.4%
5.98 118
 
1.3%
2.88 92
 
1.0%
30.98 86
 
0.9%
20.99 78
 
0.8%
35.99 78
 
0.8%
205.99 70
 
0.7%
Other values (741) 8103
86.0%
ValueCountFrequency (%)
0.99 2
 
< 0.1%
1.14 13
 
0.1%
1.26 14
0.1%
1.48 13
 
0.1%
1.6 6
 
0.1%
1.68 24
0.3%
1.7 9
 
0.1%
1.74 10
 
0.1%
1.76 34
0.4%
1.8 3
 
< 0.1%
ValueCountFrequency (%)
6783.02 7
0.1%
3502.14 6
0.1%
3499.99 8
0.1%
2550.14 7
0.1%
2036.48 8
0.1%
1938.02 8
0.1%
1889.99 3
 
< 0.1%
1637.53 2
 
< 0.1%
1500.97 6
0.1%
1360.14 4
< 0.1%

Shipping Cost
Real number (ℝ)

Distinct652
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.795142
Minimum0.49
Maximum164.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:10.421734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.49
5-th percentile0.8
Q13.1925
median6.05
Q313.99
95-th percentile55.285
Maximum164.73
Range164.24
Interquartile range (IQR)10.7975

Descriptive statistics

Standard deviation17.181203
Coefficient of variation (CV)1.3427911
Kurtosis7.6460144
Mean12.795142
Median Absolute Deviation (MAD)3.63
Skewness2.5445189
Sum120607.01
Variance295.19373
MonotonicityNot monotonic
2024-07-05T17:34:10.524488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.99 398
 
4.2%
8.99 366
 
3.9%
1.99 278
 
2.9%
0.5 217
 
2.3%
0.99 167
 
1.8%
4 158
 
1.7%
0.7 155
 
1.6%
1.49 155
 
1.6%
24.49 149
 
1.6%
2.99 142
 
1.5%
Other values (642) 7241
76.8%
ValueCountFrequency (%)
0.49 37
 
0.4%
0.5 217
2.3%
0.7 155
1.6%
0.71 24
 
0.3%
0.73 1
 
< 0.1%
0.75 8
 
0.1%
0.76 8
 
0.1%
0.78 8
 
0.1%
0.79 3
 
< 0.1%
0.8 25
 
0.3%
ValueCountFrequency (%)
164.73 1
 
< 0.1%
154.12 1
 
< 0.1%
147.12 2
 
< 0.1%
143.71 1
 
< 0.1%
130 1
 
< 0.1%
126 1
 
< 0.1%
110.2 12
0.1%
99 7
0.1%
91.05 8
0.1%
89.3 14
0.1%

Customer ID
Real number (ℝ)

Distinct2703
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1738.4222
Minimum2
Maximum3403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:10.652488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile181
Q1898
median1750
Q32578.75
95-th percentile3238.75
Maximum3403
Range3401
Interquartile range (IQR)1680.75

Descriptive statistics

Standard deviation979.1672
Coefficient of variation (CV)0.5632505
Kurtosis-1.1834638
Mean1738.4222
Median Absolute Deviation (MAD)837.5
Skewness-0.047717047
Sum16386368
Variance958768.4
MonotonicityNot monotonic
2024-07-05T17:34:10.806237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1193 27
 
0.3%
699 26
 
0.3%
2107 22
 
0.2%
2491 22
 
0.2%
2882 21
 
0.2%
308 21
 
0.2%
3079 20
 
0.2%
272 19
 
0.2%
1999 19
 
0.2%
1129 18
 
0.2%
Other values (2693) 9211
97.7%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 6
0.1%
5 2
 
< 0.1%
6 4
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
3403 2
 
< 0.1%
3402 4
< 0.1%
3400 7
0.1%
3399 4
< 0.1%
3398 3
< 0.1%
3397 6
0.1%
3396 7
0.1%
3394 4
< 0.1%
3393 5
0.1%
3391 1
 
< 0.1%
Distinct2703
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:11.041501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length28
Median length25
Mean length13.185445
Min length6

Characters and Unicode

Total characters124286
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique897 ?
Unique (%)9.5%

Sample

1st rowAnita Hahn
2nd rowAlex Nicholson
3rd rowAlex Nicholson
4th rowJane Shah
5th rowEllen McCormick
ValueCountFrequency (%)
james 60
 
0.3%
norman 59
 
0.3%
keith 58
 
0.3%
dana 56
 
0.3%
ricky 56
 
0.3%
andrew 56
 
0.3%
lindsay 55
 
0.3%
herbert 52
 
0.3%
sean 52
 
0.3%
ross 51
 
0.3%
Other values (1375) 19300
97.2%
2024-07-05T17:34:11.320113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 12459
 
10.0%
10429
 
8.4%
a 10240
 
8.2%
n 10235
 
8.2%
r 8953
 
7.2%
l 6776
 
5.5%
o 6752
 
5.4%
i 6681
 
5.4%
s 4562
 
3.7%
t 4323
 
3.5%
Other values (44) 42876
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 93505
75.2%
Uppercase Letter 20263
 
16.3%
Space Separator 10429
 
8.4%
Other Punctuation 89
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12459
13.3%
a 10240
11.0%
n 10235
10.9%
r 8953
9.6%
l 6776
 
7.2%
o 6752
 
7.2%
i 6681
 
7.1%
s 4562
 
4.9%
t 4323
 
4.6%
y 3631
 
3.9%
Other values (16) 18893
20.2%
Uppercase Letter
ValueCountFrequency (%)
B 1859
 
9.2%
M 1628
 
8.0%
C 1541
 
7.6%
S 1499
 
7.4%
H 1405
 
6.9%
J 1245
 
6.1%
R 1183
 
5.8%
G 1081
 
5.3%
L 1061
 
5.2%
K 972
 
4.8%
Other values (16) 6789
33.5%
Space Separator
ValueCountFrequency (%)
10429
100.0%
Other Punctuation
ValueCountFrequency (%)
' 89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 113768
91.5%
Common 10518
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12459
 
11.0%
a 10240
 
9.0%
n 10235
 
9.0%
r 8953
 
7.9%
l 6776
 
6.0%
o 6752
 
5.9%
i 6681
 
5.9%
s 4562
 
4.0%
t 4323
 
3.8%
y 3631
 
3.2%
Other values (42) 39156
34.4%
Common
ValueCountFrequency (%)
10429
99.2%
' 89
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12459
 
10.0%
10429
 
8.4%
a 10240
 
8.2%
n 10235
 
8.2%
r 8953
 
7.2%
l 6776
 
5.5%
o 6752
 
5.4%
i 6681
 
5.4%
s 4562
 
3.7%
t 4323
 
3.5%
Other values (44) 42876
34.5%

Ship Mode
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
Regular Air
7036 
Delivery Truck
1283 
Express Air
1107 

Length

Max length14
Median length11
Mean length11.408339
Min length11

Characters and Unicode

Total characters107535
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExpress Air
2nd rowRegular Air
3rd rowExpress Air
4th rowRegular Air
5th rowRegular Air

Common Values

ValueCountFrequency (%)
Regular Air 7036
74.6%
Delivery Truck 1283
 
13.6%
Express Air 1107
 
11.7%

Length

2024-07-05T17:34:11.449409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T17:34:11.549399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
air 8143
43.2%
regular 7036
37.3%
delivery 1283
 
6.8%
truck 1283
 
6.8%
express 1107
 
5.9%

Most occurring characters

ValueCountFrequency (%)
r 18852
17.5%
e 10709
10.0%
9426
8.8%
i 9426
8.8%
u 8319
7.7%
l 8319
7.7%
A 8143
7.6%
R 7036
 
6.5%
g 7036
 
6.5%
a 7036
 
6.5%
Other values (10) 13233
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79257
73.7%
Uppercase Letter 18852
 
17.5%
Space Separator 9426
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 18852
23.8%
e 10709
13.5%
i 9426
11.9%
u 8319
10.5%
l 8319
10.5%
g 7036
 
8.9%
a 7036
 
8.9%
s 2214
 
2.8%
v 1283
 
1.6%
y 1283
 
1.6%
Other values (4) 4780
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
A 8143
43.2%
R 7036
37.3%
T 1283
 
6.8%
D 1283
 
6.8%
E 1107
 
5.9%
Space Separator
ValueCountFrequency (%)
9426
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98109
91.2%
Common 9426
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 18852
19.2%
e 10709
10.9%
i 9426
9.6%
u 8319
8.5%
l 8319
8.5%
A 8143
8.3%
R 7036
 
7.2%
g 7036
 
7.2%
a 7036
 
7.2%
s 2214
 
2.3%
Other values (9) 11019
11.2%
Common
ValueCountFrequency (%)
9426
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 18852
17.5%
e 10709
10.0%
9426
8.8%
i 9426
8.8%
u 8319
7.7%
l 8319
7.7%
A 8143
7.6%
R 7036
 
6.5%
g 7036
 
6.5%
a 7036
 
6.5%
Other values (10) 13233
12.3%

Customer Segment
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
Corporate
3375 
Home Office
2316 
Consumer
1894 
Small Business
1841 

Length

Max length14
Median length11
Mean length10.267027
Min length8

Characters and Unicode

Total characters96777
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome Office
2nd rowConsumer
3rd rowConsumer
4th rowConsumer
5th rowCorporate

Common Values

ValueCountFrequency (%)
Corporate 3375
35.8%
Home Office 2316
24.6%
Consumer 1894
20.1%
Small Business 1841
19.5%

Length

2024-07-05T17:34:11.639746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T17:34:11.741674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
corporate 3375
24.8%
home 2316
17.1%
office 2316
17.1%
consumer 1894
13.9%
small 1841
13.6%
business 1841
13.6%

Most occurring characters

ValueCountFrequency (%)
e 11742
12.1%
o 10960
 
11.3%
r 8644
 
8.9%
s 7417
 
7.7%
m 6051
 
6.3%
C 5269
 
5.4%
a 5216
 
5.4%
f 4632
 
4.8%
4157
 
4.3%
i 4157
 
4.3%
Other values (10) 28532
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79037
81.7%
Uppercase Letter 13583
 
14.0%
Space Separator 4157
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11742
14.9%
o 10960
13.9%
r 8644
10.9%
s 7417
9.4%
m 6051
7.7%
a 5216
6.6%
f 4632
 
5.9%
i 4157
 
5.3%
u 3735
 
4.7%
n 3735
 
4.7%
Other values (4) 12748
16.1%
Uppercase Letter
ValueCountFrequency (%)
C 5269
38.8%
O 2316
17.1%
H 2316
17.1%
S 1841
 
13.6%
B 1841
 
13.6%
Space Separator
ValueCountFrequency (%)
4157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92620
95.7%
Common 4157
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11742
12.7%
o 10960
11.8%
r 8644
 
9.3%
s 7417
 
8.0%
m 6051
 
6.5%
C 5269
 
5.7%
a 5216
 
5.6%
f 4632
 
5.0%
i 4157
 
4.5%
u 3735
 
4.0%
Other values (9) 24797
26.8%
Common
ValueCountFrequency (%)
4157
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96777
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11742
12.1%
o 10960
 
11.3%
r 8644
 
8.9%
s 7417
 
7.7%
m 6051
 
6.3%
C 5269
 
5.4%
a 5216
 
5.4%
f 4632
 
4.8%
4157
 
4.3%
i 4157
 
4.3%
Other values (10) 28532
29.5%

Product Category
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
Office Supplies
5181 
Technology
2312 
Furniture
1933 

Length

Max length15
Median length15
Mean length12.543178
Min length9

Characters and Unicode

Total characters118232
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffice Supplies
2nd rowOffice Supplies
3rd rowOffice Supplies
4th rowOffice Supplies
5th rowOffice Supplies

Common Values

ValueCountFrequency (%)
Office Supplies 5181
55.0%
Technology 2312
24.5%
Furniture 1933
 
20.5%

Length

2024-07-05T17:34:11.824668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T17:34:11.905714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
office 5181
35.5%
supplies 5181
35.5%
technology 2312
15.8%
furniture 1933
 
13.2%

Most occurring characters

ValueCountFrequency (%)
e 14607
12.4%
i 12295
 
10.4%
p 10362
 
8.8%
f 10362
 
8.8%
u 9047
 
7.7%
c 7493
 
6.3%
l 7493
 
6.3%
O 5181
 
4.4%
s 5181
 
4.4%
S 5181
 
4.4%
Other values (10) 31030
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98444
83.3%
Uppercase Letter 14607
 
12.4%
Space Separator 5181
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14607
14.8%
i 12295
12.5%
p 10362
10.5%
f 10362
10.5%
u 9047
9.2%
c 7493
7.6%
l 7493
7.6%
s 5181
 
5.3%
o 4624
 
4.7%
n 4245
 
4.3%
Other values (5) 12735
12.9%
Uppercase Letter
ValueCountFrequency (%)
O 5181
35.5%
S 5181
35.5%
T 2312
15.8%
F 1933
 
13.2%
Space Separator
ValueCountFrequency (%)
5181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 113051
95.6%
Common 5181
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14607
12.9%
i 12295
10.9%
p 10362
9.2%
f 10362
9.2%
u 9047
 
8.0%
c 7493
 
6.6%
l 7493
 
6.6%
O 5181
 
4.6%
s 5181
 
4.6%
S 5181
 
4.6%
Other values (9) 25849
22.9%
Common
ValueCountFrequency (%)
5181
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14607
12.4%
i 12295
 
10.4%
p 10362
 
8.8%
f 10362
 
8.8%
u 9047
 
7.7%
c 7493
 
6.3%
l 7493
 
6.3%
O 5181
 
4.4%
s 5181
 
4.4%
S 5181
 
4.4%
Other values (10) 31030
26.2%
Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
Paper
1379 
Binders and Binder Accessories
1028 
Telephones and Communication
992 
Office Furnishings
883 
Computer Peripherals
846 
Other values (12)
4298 

Length

Max length30
Median length22
Mean length17.072884
Min length5

Characters and Unicode

Total characters160929
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStorage & Organization
2nd rowPaper
3rd rowPaper
4th rowBinders and Binder Accessories
5th rowStorage & Organization

Common Values

ValueCountFrequency (%)
Paper 1379
14.6%
Binders and Binder Accessories 1028
10.9%
Telephones and Communication 992
10.5%
Office Furnishings 883
9.4%
Computer Peripherals 846
9.0%
Pens & Art Supplies 721
7.6%
Storage & Organization 610
 
6.5%
Appliances 492
 
5.2%
Chairs & Chairmats 440
 
4.7%
Tables 404
 
4.3%
Other values (7) 1631
17.3%

Length

2024-07-05T17:34:11.983727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 2273
 
10.5%
1771
 
8.2%
paper 1379
 
6.3%
office 1259
 
5.8%
binders 1028
 
4.7%
binder 1028
 
4.7%
accessories 1028
 
4.7%
telephones 992
 
4.6%
communication 992
 
4.6%
furnishings 883
 
4.1%
Other values (22) 9085
41.8%

Most occurring characters

ValueCountFrequency (%)
e 17270
 
10.7%
s 13391
 
8.3%
i 13036
 
8.1%
n 12347
 
7.7%
12292
 
7.6%
r 11618
 
7.2%
a 10740
 
6.7%
o 7007
 
4.4%
p 6859
 
4.3%
c 5536
 
3.4%
Other values (27) 50833
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 129037
80.2%
Uppercase Letter 17674
 
11.0%
Space Separator 12292
 
7.6%
Other Punctuation 1926
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17270
13.4%
s 13391
10.4%
i 13036
10.1%
n 12347
9.6%
r 11618
9.0%
a 10740
8.3%
o 7007
 
5.4%
p 6859
 
5.3%
c 5536
 
4.3%
d 4524
 
3.5%
Other values (12) 26709
20.7%
Uppercase Letter
ValueCountFrequency (%)
P 2946
16.7%
C 2816
15.9%
B 2457
13.9%
A 2241
12.7%
O 1869
10.6%
T 1551
8.8%
S 1486
8.4%
F 981
 
5.6%
M 376
 
2.1%
R 350
 
2.0%
Other values (2) 601
 
3.4%
Other Punctuation
ValueCountFrequency (%)
& 1771
92.0%
, 155
 
8.0%
Space Separator
ValueCountFrequency (%)
12292
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 146711
91.2%
Common 14218
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17270
 
11.8%
s 13391
 
9.1%
i 13036
 
8.9%
n 12347
 
8.4%
r 11618
 
7.9%
a 10740
 
7.3%
o 7007
 
4.8%
p 6859
 
4.7%
c 5536
 
3.8%
d 4524
 
3.1%
Other values (24) 44383
30.3%
Common
ValueCountFrequency (%)
12292
86.5%
& 1771
 
12.5%
, 155
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17270
 
10.7%
s 13391
 
8.3%
i 13036
 
8.1%
n 12347
 
7.7%
12292
 
7.6%
r 11618
 
7.2%
a 10740
 
6.7%
o 7007
 
4.4%
p 6859
 
4.3%
c 5536
 
3.4%
Other values (27) 50833
31.6%
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
Small Box
4887 
Wrap Bag
1312 
Small Pack
1067 
Jumbo Drum
703 
Jumbo Box
590 
Other values (2)
867 

Length

Max length10
Median length9
Mean length9.0920857
Min length8

Characters and Unicode

Total characters85702
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLarge Box
2nd rowWrap Bag
3rd rowWrap Bag
4th rowSmall Box
5th rowSmall Box

Common Values

ValueCountFrequency (%)
Small Box 4887
51.8%
Wrap Bag 1312
 
13.9%
Small Pack 1067
 
11.3%
Jumbo Drum 703
 
7.5%
Jumbo Box 590
 
6.3%
Large Box 457
 
4.8%
Medium Box 410
 
4.3%

Length

2024-07-05T17:34:12.059527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T17:34:12.181800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
box 6344
33.7%
small 5954
31.6%
wrap 1312
 
7.0%
bag 1312
 
7.0%
jumbo 1293
 
6.9%
pack 1067
 
5.7%
drum 703
 
3.7%
large 457
 
2.4%
medium 410
 
2.2%

Most occurring characters

ValueCountFrequency (%)
l 11908
13.9%
a 10102
11.8%
9426
11.0%
m 8360
9.8%
B 7656
8.9%
o 7637
8.9%
x 6344
7.4%
S 5954
6.9%
r 2472
 
2.9%
u 2406
 
2.8%
Other values (14) 13437
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57424
67.0%
Uppercase Letter 18852
 
22.0%
Space Separator 9426
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 11908
20.7%
a 10102
17.6%
m 8360
14.6%
o 7637
13.3%
x 6344
11.0%
r 2472
 
4.3%
u 2406
 
4.2%
g 1769
 
3.1%
p 1312
 
2.3%
b 1293
 
2.3%
Other values (5) 3821
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 7656
40.6%
S 5954
31.6%
W 1312
 
7.0%
J 1293
 
6.9%
P 1067
 
5.7%
D 703
 
3.7%
L 457
 
2.4%
M 410
 
2.2%
Space Separator
ValueCountFrequency (%)
9426
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76276
89.0%
Common 9426
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 11908
15.6%
a 10102
13.2%
m 8360
11.0%
B 7656
10.0%
o 7637
10.0%
x 6344
8.3%
S 5954
7.8%
r 2472
 
3.2%
u 2406
 
3.2%
g 1769
 
2.3%
Other values (13) 11668
15.3%
Common
ValueCountFrequency (%)
9426
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 11908
13.9%
a 10102
11.8%
9426
11.0%
m 8360
9.8%
B 7656
8.9%
o 7637
8.9%
x 6344
7.4%
S 5954
6.9%
r 2472
 
2.9%
u 2406
 
2.8%
Other values (14) 13437
15.7%
Distinct1263
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:12.379391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length98
Median length75
Mean length34.331318
Min length3

Characters and Unicode

Total characters323607
Distinct characters84
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)0.5%

Sample

1st rowSafco Industrial Wire Shelving
2nd rowWhite GlueTop Scratch Pads
3rd rowBlack Print Carbonless Snap-Off® Rapid Letter, 8 1/2" x 7"
4th rowAvery Trapezoid Ring Binder, 3" Capacity, Black, 1040 sheets
5th rowDual Level, Single-Width Filing Carts
ValueCountFrequency (%)
xerox 867
 
1.8%
x 555
 
1.2%
avery 481
 
1.0%
with 459
 
1.0%
374
 
0.8%
black 373
 
0.8%
binders 347
 
0.7%
for 344
 
0.7%
chair 309
 
0.6%
staples 293
 
0.6%
Other values (2076) 43617
90.8%
2024-07-05T17:34:12.686128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38593
 
11.9%
e 29027
 
9.0%
r 17810
 
5.5%
o 17376
 
5.4%
a 16384
 
5.1%
i 15730
 
4.9%
l 14049
 
4.3%
t 13937
 
4.3%
n 13239
 
4.1%
s 12819
 
4.0%
Other values (74) 134643
41.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 206796
63.9%
Uppercase Letter 48049
 
14.8%
Space Separator 38593
 
11.9%
Decimal Number 18604
 
5.7%
Other Punctuation 7091
 
2.2%
Dash Punctuation 2608
 
0.8%
Other Symbol 1552
 
0.5%
Final Punctuation 81
 
< 0.1%
Open Punctuation 78
 
< 0.1%
Close Punctuation 78
 
< 0.1%
Other values (2) 77
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 29027
14.0%
r 17810
 
8.6%
o 17376
 
8.4%
a 16384
 
7.9%
i 15730
 
7.6%
l 14049
 
6.8%
t 13937
 
6.7%
n 13239
 
6.4%
s 12819
 
6.2%
c 8356
 
4.0%
Other values (17) 48069
23.2%
Uppercase Letter
ValueCountFrequency (%)
S 5100
 
10.6%
C 4984
 
10.4%
P 4489
 
9.3%
B 4315
 
9.0%
D 2924
 
6.1%
A 2784
 
5.8%
M 2693
 
5.6%
F 2254
 
4.7%
T 2240
 
4.7%
R 1856
 
3.9%
Other values (16) 14410
30.0%
Decimal Number
ValueCountFrequency (%)
1 3592
19.3%
0 3097
16.6%
2 2239
12.0%
3 1713
9.2%
8 1593
8.6%
4 1571
8.4%
9 1415
 
7.6%
5 1274
 
6.8%
6 1065
 
5.7%
7 1045
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 3177
44.8%
/ 1515
21.4%
" 1186
 
16.7%
. 569
 
8.0%
& 240
 
3.4%
' 171
 
2.4%
# 112
 
1.6%
* 68
 
1.0%
% 48
 
0.7%
; 5
 
0.1%
Other Symbol
ValueCountFrequency (%)
® 1011
65.1%
541
34.9%
Open Punctuation
ValueCountFrequency (%)
( 60
76.9%
[ 18
 
23.1%
Close Punctuation
ValueCountFrequency (%)
) 60
76.9%
] 18
 
23.1%
Space Separator
ValueCountFrequency (%)
38593
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2608
100.0%
Final Punctuation
ValueCountFrequency (%)
81
100.0%
Math Symbol
ValueCountFrequency (%)
+ 42
100.0%
Initial Punctuation
ValueCountFrequency (%)
35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 254845
78.8%
Common 68762
 
21.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 29027
 
11.4%
r 17810
 
7.0%
o 17376
 
6.8%
a 16384
 
6.4%
i 15730
 
6.2%
l 14049
 
5.5%
t 13937
 
5.5%
n 13239
 
5.2%
s 12819
 
5.0%
c 8356
 
3.3%
Other values (43) 96118
37.7%
Common
ValueCountFrequency (%)
38593
56.1%
1 3592
 
5.2%
, 3177
 
4.6%
0 3097
 
4.5%
- 2608
 
3.8%
2 2239
 
3.3%
3 1713
 
2.5%
8 1593
 
2.3%
4 1571
 
2.3%
/ 1515
 
2.2%
Other values (21) 9064
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 321937
99.5%
None 1013
 
0.3%
Letterlike Symbols 541
 
0.2%
Punctuation 116
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38593
 
12.0%
e 29027
 
9.0%
r 17810
 
5.5%
o 17376
 
5.4%
a 16384
 
5.1%
i 15730
 
4.9%
l 14049
 
4.4%
t 13937
 
4.3%
n 13239
 
4.1%
s 12819
 
4.0%
Other values (69) 132973
41.3%
None
ValueCountFrequency (%)
® 1011
99.8%
à 2
 
0.2%
Letterlike Symbols
ValueCountFrequency (%)
541
100.0%
Punctuation
ValueCountFrequency (%)
81
69.8%
35
30.2%

Product Base Margin
Real number (ℝ)

Distinct51
Distinct (%)0.5%
Missing72
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean0.51218944
Minimum0.35
Maximum0.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:12.807087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile0.36
Q10.38
median0.52
Q30.59
95-th percentile0.78
Maximum0.85
Range0.5
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.13522874
Coefficient of variation (CV)0.26402095
Kurtosis-0.65350818
Mean0.51218944
Median Absolute Deviation (MAD)0.12
Skewness0.56124764
Sum4791.02
Variance0.018286813
MonotonicityNot monotonic
2024-07-05T17:34:12.917659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.37 855
 
9.1%
0.38 754
 
8.0%
0.36 695
 
7.4%
0.59 563
 
6.0%
0.39 541
 
5.7%
0.57 523
 
5.5%
0.56 510
 
5.4%
0.4 469
 
5.0%
0.58 430
 
4.6%
0.55 355
 
3.8%
Other values (41) 3659
38.8%
ValueCountFrequency (%)
0.35 296
 
3.1%
0.36 695
7.4%
0.37 855
9.1%
0.38 754
8.0%
0.39 541
5.7%
0.4 469
5.0%
0.41 115
 
1.2%
0.42 86
 
0.9%
0.43 117
 
1.2%
0.44 107
 
1.1%
ValueCountFrequency (%)
0.85 40
 
0.4%
0.84 29
 
0.3%
0.83 90
1.0%
0.82 34
 
0.4%
0.81 82
0.9%
0.8 55
0.6%
0.79 74
0.8%
0.78 100
1.1%
0.77 77
0.8%
0.76 60
0.6%

Region
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
Central
2899 
East
2289 
West
2284 
South
1954 

Length

Max length7
Median length5
Mean length5.1299597
Min length4

Characters and Unicode

Total characters48355
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast
2nd rowWest
3rd rowWest
4th rowCentral
5th rowWest

Common Values

ValueCountFrequency (%)
Central 2899
30.8%
East 2289
24.3%
West 2284
24.2%
South 1954
20.7%

Length

2024-07-05T17:34:13.010388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T17:34:13.101913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
central 2899
30.8%
east 2289
24.3%
west 2284
24.2%
south 1954
20.7%

Most occurring characters

ValueCountFrequency (%)
t 9426
19.5%
a 5188
10.7%
e 5183
10.7%
s 4573
9.5%
C 2899
 
6.0%
n 2899
 
6.0%
r 2899
 
6.0%
l 2899
 
6.0%
E 2289
 
4.7%
W 2284
 
4.7%
Other values (4) 7816
16.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38929
80.5%
Uppercase Letter 9426
 
19.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 9426
24.2%
a 5188
13.3%
e 5183
13.3%
s 4573
11.7%
n 2899
 
7.4%
r 2899
 
7.4%
l 2899
 
7.4%
o 1954
 
5.0%
u 1954
 
5.0%
h 1954
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
C 2899
30.8%
E 2289
24.3%
W 2284
24.2%
S 1954
20.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 48355
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 9426
19.5%
a 5188
10.7%
e 5183
10.7%
s 4573
9.5%
C 2899
 
6.0%
n 2899
 
6.0%
r 2899
 
6.0%
l 2899
 
6.0%
E 2289
 
4.7%
W 2284
 
4.7%
Other values (4) 7816
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48355
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 9426
19.5%
a 5188
10.7%
e 5183
10.7%
s 4573
9.5%
C 2899
 
6.0%
n 2899
 
6.0%
r 2899
 
6.0%
l 2899
 
6.0%
E 2289
 
4.7%
W 2284
 
4.7%
Other values (4) 7816
16.2%
Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
California
1021 
Texas
646 
Illinois
 
584
New York
 
574
Florida
 
522
Other values (44)
6079 

Length

Max length20
Median length13
Mean length8.2980055
Min length4

Characters and Unicode

Total characters78217
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaryland
2nd rowCalifornia
3rd rowCalifornia
4th rowMinnesota
5th rowCalifornia

Common Values

ValueCountFrequency (%)
California 1021
 
10.8%
Texas 646
 
6.9%
Illinois 584
 
6.2%
New York 574
 
6.1%
Florida 522
 
5.5%
Ohio 396
 
4.2%
Michigan 327
 
3.5%
Washington 327
 
3.5%
Pennsylvania 271
 
2.9%
North Carolina 251
 
2.7%
Other values (39) 4507
47.8%

Length

2024-07-05T17:34:13.199189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 1021
 
9.3%
new 889
 
8.1%
texas 646
 
5.9%
illinois 584
 
5.3%
york 574
 
5.3%
florida 522
 
4.8%
ohio 396
 
3.6%
carolina 356
 
3.3%
michigan 327
 
3.0%
washington 327
 
3.0%
Other values (43) 5290
48.4%

Most occurring characters

ValueCountFrequency (%)
a 9856
12.6%
i 9010
 
11.5%
n 7026
 
9.0%
o 6792
 
8.7%
s 5299
 
6.8%
r 4606
 
5.9%
e 4372
 
5.6%
l 4025
 
5.1%
t 2172
 
2.8%
h 2128
 
2.7%
Other values (36) 22931
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65847
84.2%
Uppercase Letter 10864
 
13.9%
Space Separator 1506
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9856
15.0%
i 9010
13.7%
n 7026
10.7%
o 6792
10.3%
s 5299
8.0%
r 4606
7.0%
e 4372
6.6%
l 4025
 
6.1%
t 2172
 
3.3%
h 2128
 
3.2%
Other values (14) 10561
16.0%
Uppercase Letter
ValueCountFrequency (%)
C 1704
15.7%
M 1466
13.5%
N 1294
11.9%
I 1115
10.3%
T 812
7.5%
O 668
 
6.1%
Y 574
 
5.3%
W 560
 
5.2%
F 522
 
4.8%
A 382
 
3.5%
Other values (11) 1767
16.3%
Space Separator
ValueCountFrequency (%)
1506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76711
98.1%
Common 1506
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9856
12.8%
i 9010
11.7%
n 7026
 
9.2%
o 6792
 
8.9%
s 5299
 
6.9%
r 4606
 
6.0%
e 4372
 
5.7%
l 4025
 
5.2%
t 2172
 
2.8%
h 2128
 
2.8%
Other values (35) 21425
27.9%
Common
ValueCountFrequency (%)
1506
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9856
12.6%
i 9010
 
11.5%
n 7026
 
9.0%
o 6792
 
8.7%
s 5299
 
6.8%
r 4606
 
5.9%
e 4372
 
5.6%
l 4025
 
5.1%
t 2172
 
2.8%
h 2128
 
2.7%
Other values (36) 22931
29.3%

City
Text

Distinct1424
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:13.394518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length16
Mean length9.1706981
Min length3

Characters and Unicode

Total characters86443
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)1.6%

Sample

1st rowBowie
2nd rowMontebello
3rd rowMontebello
4th rowPrior Lake
5th rowNapa
ValueCountFrequency (%)
city 468
 
3.6%
new 264
 
2.1%
los 222
 
1.7%
york 208
 
1.6%
angeles 203
 
1.6%
park 165
 
1.3%
san 142
 
1.1%
beach 116
 
0.9%
west 112
 
0.9%
heights 107
 
0.8%
Other values (1378) 10855
84.4%
2024-07-05T17:34:13.707345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8135
 
9.4%
a 7413
 
8.6%
o 6517
 
7.5%
n 6213
 
7.2%
l 5597
 
6.5%
i 5242
 
6.1%
r 5232
 
6.1%
t 4733
 
5.5%
s 3999
 
4.6%
3436
 
4.0%
Other values (42) 29926
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70145
81.1%
Uppercase Letter 12862
 
14.9%
Space Separator 3436
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8135
11.6%
a 7413
10.6%
o 6517
9.3%
n 6213
8.9%
l 5597
 
8.0%
i 5242
 
7.5%
r 5232
 
7.5%
t 4733
 
6.7%
s 3999
 
5.7%
d 2049
 
2.9%
Other values (16) 15015
21.4%
Uppercase Letter
ValueCountFrequency (%)
C 1540
 
12.0%
S 1133
 
8.8%
P 1004
 
7.8%
B 923
 
7.2%
M 894
 
7.0%
L 884
 
6.9%
H 744
 
5.8%
A 701
 
5.5%
W 623
 
4.8%
R 548
 
4.3%
Other values (15) 3868
30.1%
Space Separator
ValueCountFrequency (%)
3436
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83007
96.0%
Common 3436
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8135
 
9.8%
a 7413
 
8.9%
o 6517
 
7.9%
n 6213
 
7.5%
l 5597
 
6.7%
i 5242
 
6.3%
r 5232
 
6.3%
t 4733
 
5.7%
s 3999
 
4.8%
d 2049
 
2.5%
Other values (41) 27877
33.6%
Common
ValueCountFrequency (%)
3436
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8135
 
9.4%
a 7413
 
8.6%
o 6517
 
7.5%
n 6213
 
7.2%
l 5597
 
6.5%
i 5242
 
6.1%
r 5232
 
6.1%
t 4733
 
5.5%
s 3999
 
4.6%
3436
 
4.0%
Other values (42) 29926
34.6%

Postal Code
Real number (ℝ)

Distinct1697
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52446.327
Minimum1001
Maximum99362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:13.843504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile5451
Q129406
median52302
Q378516
95-th percentile97035
Maximum99362
Range98361
Interquartile range (IQR)49110

Descriptive statistics

Standard deviation29374.598
Coefficient of variation (CV)0.56008875
Kurtosis-1.208889
Mean52446.327
Median Absolute Deviation (MAD)24714
Skewness-0.042557958
Sum4.9435908 × 108
Variance8.62867 × 108
MonotonicityNot monotonic
2024-07-05T17:34:13.936667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10177 54
 
0.6%
90049 47
 
0.5%
20016 37
 
0.4%
60601 36
 
0.4%
90045 33
 
0.4%
2113 29
 
0.3%
98115 27
 
0.3%
90041 26
 
0.3%
30318 24
 
0.3%
98119 23
 
0.2%
Other values (1687) 9090
96.4%
ValueCountFrequency (%)
1001 1
< 0.1%
1007 1
< 0.1%
1013 1
< 0.1%
1027 1
< 0.1%
1028 1
< 0.1%
1040 1
< 0.1%
1056 1
< 0.1%
1060 1
< 0.1%
1069 1
< 0.1%
1075 2
< 0.1%
ValueCountFrequency (%)
99362 8
0.1%
99352 5
0.1%
99336 6
0.1%
99301 6
0.1%
99207 7
0.1%
99163 7
0.1%
98902 3
 
< 0.1%
98801 7
0.1%
98661 8
0.1%
98632 6
0.1%
Distinct1419
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
Minimum2010-01-01 00:00:00
Maximum2013-12-31 00:00:00
2024-07-05T17:34:14.036182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:14.165043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1450
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
Minimum2010-01-02 00:00:00
Maximum2014-01-17 00:00:00
2024-07-05T17:34:14.304688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:14.449090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Profit
Real number (ℝ)

Distinct8984
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.23641
Minimum-16476.838
Maximum16332.414
Zeros0
Zeros (%)0.0%
Negative4558
Negative (%)48.4%
Memory size73.8 KiB
2024-07-05T17:34:14.549050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-16476.838
5-th percentile-545.82
Q1-74.017375
median2.5676
Q3140.24385
95-th percentile1322.366
Maximum16332.414
Range32809.252
Interquartile range (IQR)214.26123

Descriptive statistics

Standard deviation998.48648
Coefficient of variation (CV)7.1711594
Kurtosis56.534342
Mean139.23641
Median Absolute Deviation (MAD)96.34976
Skewness0.84141147
Sum1312442.4
Variance996975.26
MonotonicityNot monotonic
2024-07-05T17:34:14.884495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-969.048366 4
 
< 0.1%
11.65095 4
 
< 0.1%
33.98895 4
 
< 0.1%
-1331.553366 3
 
< 0.1%
-1356.667712 3
 
< 0.1%
-33.11 3
 
< 0.1%
-106.421 3
 
< 0.1%
-44.86 3
 
< 0.1%
-433.290143 3
 
< 0.1%
6.79 3
 
< 0.1%
Other values (8974) 9393
99.6%
ValueCountFrequency (%)
-16476.838 1
< 0.1%
-14369.12358 1
< 0.1%
-14140.7016 1
< 0.1%
-13706.464 1
< 0.1%
-13562.63741 1
< 0.1%
-10402.94392 1
< 0.1%
-10263.6597 1
< 0.1%
-9078.94 1
< 0.1%
-8570.4483 1
< 0.1%
-7961.4309 1
< 0.1%
ValueCountFrequency (%)
16332.414 1
< 0.1%
12504.9045 1
< 0.1%
11429.4774 1
< 0.1%
9228.2256 1
< 0.1%
9195.975 1
< 0.1%
8917.7187 1
< 0.1%
8798.1831 1
< 0.1%
8752.0428 1
< 0.1%
8202.5682 1
< 0.1%
8118.8919 1
< 0.1%

Quantity ordered new
Real number (ℝ)

Distinct112
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.79843
Minimum1
Maximum170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:15.009738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q317
95-th percentile42
Maximum170
Range169
Interquartile range (IQR)12

Descriptive statistics

Standard deviation15.107688
Coefficient of variation (CV)1.0948846
Kurtosis19.004728
Mean13.79843
Median Absolute Deviation (MAD)5
Skewness3.52665
Sum130064
Variance228.24223
MonotonicityNot monotonic
2024-07-05T17:34:15.106960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 628
 
6.7%
9 499
 
5.3%
5 499
 
5.3%
12 498
 
5.3%
11 498
 
5.3%
3 490
 
5.2%
8 487
 
5.2%
2 482
 
5.1%
4 448
 
4.8%
7 446
 
4.7%
Other values (102) 4451
47.2%
ValueCountFrequency (%)
1 628
6.7%
2 482
5.1%
3 490
5.2%
4 448
4.8%
5 499
5.3%
6 438
4.6%
7 446
4.7%
8 487
5.2%
9 499
5.3%
10 444
4.7%
ValueCountFrequency (%)
170 1
 
< 0.1%
167 2
< 0.1%
162 1
 
< 0.1%
160 1
 
< 0.1%
155 1
 
< 0.1%
151 1
 
< 0.1%
148 1
 
< 0.1%
146 3
< 0.1%
139 3
< 0.1%
137 2
< 0.1%

Sales
Real number (ℝ)

Distinct8674
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean949.70627
Minimum1.32
Maximum100119.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:15.224306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.32
5-th percentile14.52
Q161.2825
median203.455
Q3776.4025
95-th percentile4209.38
Maximum100119.16
Range100117.84
Interquartile range (IQR)715.12

Descriptive statistics

Standard deviation2598.0198
Coefficient of variation (CV)2.7356035
Kurtosis300.12979
Mean949.70627
Median Absolute Deviation (MAD)174.405
Skewness12.212531
Sum8951931.3
Variance6749707
MonotonicityNot monotonic
2024-07-05T17:34:15.349497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.85 4
 
< 0.1%
33.53 4
 
< 0.1%
80.58 4
 
< 0.1%
14.53 4
 
< 0.1%
119.86 4
 
< 0.1%
10.48 4
 
< 0.1%
21.9 3
 
< 0.1%
33.3 3
 
< 0.1%
36.16 3
 
< 0.1%
9.64 3
 
< 0.1%
Other values (8664) 9390
99.6%
ValueCountFrequency (%)
1.32 1
< 0.1%
1.62 1
< 0.1%
1.65 1
< 0.1%
2.24 1
< 0.1%
2.25 2
< 0.1%
2.66 1
< 0.1%
2.74 1
< 0.1%
2.77 1
< 0.1%
2.87 1
< 0.1%
3.07 1
< 0.1%
ValueCountFrequency (%)
100119.16 1
< 0.1%
50332.66 1
< 0.1%
48418.58 1
< 0.1%
45737.33 1
< 0.1%
43046.2 1
< 0.1%
40136.93 1
< 0.1%
36532.46 1
< 0.1%
35147.33 1
< 0.1%
32589.59 1
< 0.1%
32510.21 1
< 0.1%

Order ID
Real number (ℝ)

Distinct6455
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82318.489
Minimum6
Maximum91591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2024-07-05T17:34:15.447341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile28822.25
Q186737.25
median88344.5
Q389987.75
95-th percentile91256
Maximum91591
Range91585
Interquartile range (IQR)3250.5

Descriptive statistics

Standard deviation19149.449
Coefficient of variation (CV)0.23262634
Kurtosis6.9533914
Mean82318.489
Median Absolute Deviation (MAD)1628
Skewness-2.8602649
Sum7.7593408 × 108
Variance3.6670139 × 108
MonotonicityNot monotonic
2024-07-05T17:34:15.557066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90571 6
 
0.1%
43745 6
 
0.1%
88060 5
 
0.1%
91485 5
 
0.1%
86939 5
 
0.1%
86510 5
 
0.1%
89027 5
 
0.1%
90082 5
 
0.1%
87033 5
 
0.1%
90113 5
 
0.1%
Other values (6445) 9374
99.4%
ValueCountFrequency (%)
6 1
 
< 0.1%
193 1
 
< 0.1%
322 2
< 0.1%
358 2
< 0.1%
359 1
 
< 0.1%
386 2
< 0.1%
388 1
 
< 0.1%
454 1
 
< 0.1%
548 3
< 0.1%
612 2
< 0.1%
ValueCountFrequency (%)
91591 3
< 0.1%
91590 2
< 0.1%
91589 2
< 0.1%
91588 1
 
< 0.1%
91587 1
 
< 0.1%
91586 1
 
< 0.1%
91585 2
< 0.1%
91584 1
 
< 0.1%
91583 1
 
< 0.1%
91582 1
 
< 0.1%

Interactions

2024-07-05T17:34:08.004326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.126350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.929868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.752365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:59.807782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:01.005380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:02.740996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:04.082847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.298787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:06.237669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.157352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:08.077821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.211328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.997144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.834620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:59.882786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:01.160404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:02.851169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:04.171667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.373123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:06.314701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.246887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:08.152617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.283154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.067066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.909820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:59.954511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:01.314822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:02.934828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:04.266235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.445483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:06.395654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.328925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:08.225580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.359439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.140173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:59.006174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:00.047941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:01.488319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:03.022512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:04.460526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.543479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:06.485774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.408579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:08.306957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.428053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.211509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:59.102398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:00.174833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:01.699591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:03.118693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:04.720732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.638000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:06.567231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.480703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:08.390752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.501311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.286335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:59.191269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:00.257316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:01.873147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:03.211134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:04.806807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.720258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:06.646074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.559631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:08.470956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.573577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.368103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:59.279965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:00.341876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:02.026694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:03.325696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:04.894213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.791134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:06.740873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.626661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:08.543703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.639587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.442133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:59.355257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:00.423556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:02.239511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:03.487469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:04.968769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.867076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:06.822939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.702621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:08.623617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.710908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.521357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:59.557503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:00.501609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:02.362543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:03.638711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.049694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.993578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:06.923500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.786338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:08.705518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.792016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.605638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:59.656971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:00.667843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:02.463712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:03.798025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.140346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:06.076648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.006731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.864617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:08.954500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:57.861533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:58.677974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:33:59.730117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:00.844677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:02.562516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:03.958513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:05.223241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:06.163439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.084423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-05T17:34:07.933700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-07-05T17:34:09.077054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T17:34:09.332399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Row IDOrder PriorityDiscountUnit PriceShipping CostCustomer IDCustomer NameShip ModeCustomer SegmentProduct CategoryProduct Sub-CategoryProduct ContainerProduct NameProduct Base MarginRegionState or ProvinceCityPostal CodeOrder DateShip DateProfitQuantity ordered newSalesOrder ID
019914Not Specified0.0895.9935.002211Anita HahnExpress AirHome OfficeOffice SuppliesStorage & OrganizationLarge BoxSafco Industrial Wire ShelvingNaNEastMarylandBowie207152010-01-012010-01-03-425.208402193.8888028
124225Critical0.0815.041.971155Alex NicholsonRegular AirConsumerOffice SuppliesPaperWrap BagWhite GlueTop Scratch Pads0.39WestCaliforniaMontebello906402010-01-022010-01-02108.5163011157.2790853
224224Critical0.099.112.151155Alex NicholsonExpress AirConsumerOffice SuppliesPaperWrap BagBlack Print Carbonless Snap-Off® Rapid Letter, 8 1/2" x 7"0.40WestCaliforniaMontebello906402010-01-022010-01-0420.29960434.4190853
319279Critical0.0640.982.99950Jane ShahRegular AirConsumerOffice SuppliesBinders and Binder AccessoriesSmall BoxAvery Trapezoid Ring Binder, 3" Capacity, Black, 1040 sheets0.36CentralMinnesotaPrior Lake553722010-01-022010-01-04-14.80188141.6089083
423274Low0.05155.067.0767Ellen McCormickRegular AirCorporateOffice SuppliesStorage & OrganizationSmall BoxDual Level, Single-Width Filing Carts0.59WestCaliforniaNapa945592010-01-022010-01-09845.6640081225.6087946
55272Low0.00291.7348.8068Scott BunnDelivery TruckCorporateFurnitureChairs & ChairmatsJumbo DrumHon 4070 Series Pagoda™ Armless Upholstered Stacking Chairs0.56EastNew YorkNew York City101772010-01-022010-01-02-308.9280041239.0637537
61279Critical0.0640.982.99949Ernest OhRegular AirConsumerOffice SuppliesBinders and Binder AccessoriesSmall BoxAvery Trapezoid Ring Binder, 3" Capacity, Black, 1040 sheets0.36WestCaliforniaLos Angeles900492010-01-022010-01-04-19.099203124.819285
75273Low0.07100.9845.0068Scott BunnDelivery TruckCorporateFurnitureChairs & ChairmatsJumbo DrumHon Valutask™ Swivel Chairs0.69EastNew YorkNew York City101772010-01-022010-01-04-1679.76000434083.1937537
85274Low0.05155.067.0768Scott BunnRegular AirCorporateOffice SuppliesStorage & OrganizationSmall BoxDual Level, Single-Width Filing Carts0.59EastNew YorkNew York City101772010-01-022010-01-09575.39600324902.3837537
923705High0.09212.6052.202579Marshall SutherlandDelivery TruckHome OfficeFurnitureTablesJumbo BoxBush Advantage Collection® Round Conference Table0.64SouthAlabamaPhenix City368692010-01-032010-01-04-274.498001174.5088296
Row IDOrder PriorityDiscountUnit PriceShipping CostCustomer IDCustomer NameShip ModeCustomer SegmentProduct CategoryProduct Sub-CategoryProduct ContainerProduct NameProduct Base MarginRegionState or ProvinceCityPostal CodeOrder DateShip DateProfitQuantity ordered newSalesOrder ID
941625142Not Specified0.1013.484.513265Glenn MorganExpress AirCorporateOffice SuppliesStorage & OrganizationSmall BoxTenex Personal Project File with Scoop Front Design, Black0.59WestCaliforniaFairfield945332013-12-302013-12-30-5.06352018230.4589840
941724916Low0.09832.8124.492149Rodney ProctorRegular AirHome OfficeOffice SuppliesScissors, Rulers and TrimmersMedium BoxMartin Yale Chadless Opener Electric Letter Opener0.83SouthKentuckyNicholasville403562013-12-302013-12-30676.0320001803.3391107
941824915Low0.0019.984.002149Rodney ProctorRegular AirHome OfficeTechnologyComputer PeripheralsSmall BoxBelkin 105-Key Black Keyboard0.68SouthKentuckyNicholasville403562013-12-302013-12-30450.32760016347.3291107
941924361Medium0.101.480.703187Sidney GilliamRegular AirConsumerOffice SuppliesRubber BandsWrap BagBinder Clips by OIC0.37SouthFloridaRiverview335692013-12-302013-12-30-415.82800057.0889038
942025141Not Specified0.0660.9830.003265Glenn MorganDelivery TruckCorporateFurnitureChairs & ChairmatsJumbo DrumNovimex Fabric Task Chair0.70WestCaliforniaFairfield945332013-12-302013-12-30-56.0985603195.5689840
942124712Not Specified0.0813.736.851672Sidney ScarboroughRegular AirSmall BusinessFurnitureOffice FurnishingsWrap BagDAX Wood Document Frame.0.54SouthVirginiaCharlottesville229012013-12-302013-12-30-233.43600023296.9486733
942221583Low0.097.284.232218Hilda FletcherExpress AirHome OfficeOffice SuppliesPaperWrap BagBlack Print Carbonless 8 1/2" x 8 1/4" Rapid Memo Book0.39CentralTexasHarker Heights765432013-12-302013-12-30-6.26976019132.2191227
942324553Not Specified0.0612.954.982277Allison PeacockRegular AirConsumerOffice SuppliesBinders and Binder AccessoriesSmall BoxGBC Binding covers0.40EastNew YorkNorth Tonawanda141202013-12-312014-01-024.045184565.1891506
942424554Not Specified0.00122.9970.202277Allison PeacockDelivery TruckConsumerFurnitureChairs & ChairmatsJumbo DrumGlobal High-Back Leather Tilter, Burgundy0.74EastNew YorkNorth Tonawanda141202013-12-312014-01-02-28.0996801200.5791506
942524129Medium0.03162.9319.991090Seth DaviesRegular AirSmall BusinessOffice SuppliesEnvelopesSmall BoxMultimedia Mailers0.39CentralIllinoisSaint Charles601742013-12-312014-01-022318.910600203360.7490144